Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. As a Staff Applied AI Engineer, you'll raise the bar for how AI gets built, governed, and evaluated across the team. You'll define standards that other engineers adopt, such as evaluation methodologies for agents and MLOps practices, while acting as the senior technical voice for the most sensitive AI decisions our clients face.
What you'll do
- Define standards for responsible AI, model governance, and production MLOps that get adopted broadly across the Global Public Sector team
- Build or validate the highest-risk parts of strategic AI systems and use those systems to establish standards other teams can adopt.
- Architect AI systems designed to prevent systemic failure, and lead the resolution of the most severe incidents tied to model safety or data integrity
- Build reusable AI capabilities, such as production-ready agent implementations built on proven architectures, fine-tuned models, or evaluation methodologies, that other engineers and clients can build on
- Advise on which new AI developments are worth adopting, and help set the technical roadmap for the domain
- Act as the senior technical partner to client leadership on AI strategy
- Coach Senior Applied AI Engineers, delegate ownership of technical domains, and build systems and practices that enable multiple teams to deliver safer AI
- Contribute to recruiting and representing Scale's AI work externally
What we look for
- 7+ years of engineering experience, with a multi-year track record owning AI/ML systems in production
- Experience judging the quality of training data, selecting the right adaptation method for a given model, evaluating fine-tuning results, and balancing serving cost, latency, and quality trade-offs
- Experience owning an AI-powered product end to end, including direct involvement in the AI's behaviour rather than implementing ML work scoped by another team
- A track record of establishing standards that outlived the projects they were built for, such as an evaluation methodology, an MLOps practice, or an architectural pattern
- Comfort operating with executive-level clients and leadership, including defending a technical position under pressure
- Deep experience with regulated, sovereign, or on-premise AI deployment, including hallucination mitigation and auditability
Where this role sits
Your primary accountability covers production AI behaviour, its backend integration, and the evaluation required for a specific customer. Senior Full-Stack Engineers own the complete user-facing application and its infrastructure. ML Research Engineers lead novel agent architecture and the benchmark methodology used across accounts. The roles work together on agents and evaluation, with accountability set by the scope of the problem.
The Public Sector context
Location, travel, and vetting requirements vary by assignment. Some UK assignments require BPSS screening and may require SC or DV clearance. Requirements depend on the account and each candidate's circumstances. We discuss assignment-specific eligibility early in the hiring process.
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.
Skills Required
- 7+ years of engineering experience with multi-year ownership of AI/ML systems in production
- Experience assessing training data quality, choosing adaptation methods, and evaluating fine-tuning results
- Experience balancing serving cost, latency, and quality trade-offs in production ML systems
- Ownership of AI-powered products end-to-end, including direct responsibility for AI behavior
- Proven track record establishing durable standards (evaluation methodologies, MLOps practices, or architectural patterns)
- Experience working with executive-level clients and defending technical decisions under pressure
- Deep experience with regulated, sovereign, or on-premise AI deployment, including hallucination mitigation and auditability
- Willingness/ability to undergo assignment-specific vetting or security screening (e.g., BPSS, SC, DV) when required
Scale AI Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Scale AI and has not been reviewed or approved by Scale AI.
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Healthcare Strength — Healthcare coverage is described as comprehensive across medical, dental, and vision, with flexibility to choose plans that fit individual or family needs. A monthly wellness stipend further supports physical and mental wellbeing expenses.
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Equity Value & Accessibility — Equity-based compensation is included in eligible packages, positioning ownership as a meaningful component of total rewards for many full-time roles. An employee stock purchase plan also provides an additional pathway to participate in potential upside.
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Leave & Time Off Breadth — Paid time off is positioned as generous with a flexible policy intended to support recharging and burnout prevention. Paid holidays and paid sick days are also part of the time-off offering.
Scale AI Insights
What We Do
Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.
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